{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 文本分析\n",
    "# 停用词    1、语料库中大量出现   2、没啥大用   3、留着过年嘛？\n",
    "# 例如： 一下， 一个， 一些，一天， 一旦    还有一些标点符号   更多的可以查看停用词表\n",
    "# TF-IDF:关键字提取，  词频和反词频进行统计\n",
    "# IDF：逆文档频率， 如果一个词比较少见，但是他在这篇文章中多次出现，那么他可能就反映了这篇文章的特性，正如我们所需要的关键词一样\n",
    " \n",
    "import pandas as  pd\n",
    "import jieba"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据源：https://tianchi.aliyun.com/dataset/dataDetail?dataId=3372 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>theme</th>\n",
       "      <th>content</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>汽车</td>\n",
       "      <td>新辉腾　４．２　Ｖ８　４座加长Ｉｎｄｉｖｉｄｕａｌ版２０１１款　最新报价</td>\n",
       "      <td>经销商　电话　试驾／订车Ｕ憬杭州滨江区江陵路１７８０号４００８－１１２２３３转５８６４＃保常...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>汽车</td>\n",
       "      <td>９１８　Ｓｐｙｄｅｒ概念车</td>\n",
       "      <td>呼叫热线　４００８－１００－３００　服务邮箱　ｋｆ＠ｐｅｏｐｌｅｄａｉｌｙ．ｃｏｍ．ｃｎ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>汽车</td>\n",
       "      <td>日内瓦亮相　ＭＩＮＩ性能版／概念车－１．６Ｔ引擎</td>\n",
       "      <td>ＭＩＮＩ品牌在二月曾经公布了最新的ＭＩＮＩ新概念车Ｃｌｕｂｖａｎ效果图，不过现在在日内瓦车展...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>汽车</td>\n",
       "      <td>清仓大甩卖一汽夏利Ｎ５威志Ｖ２低至３．３９万</td>\n",
       "      <td>清仓大甩卖！一汽夏利Ｎ５、威志Ｖ２低至３．３９万＝日，启新中国一汽强势推出一汽夏利Ｎ５、威志...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>汽车</td>\n",
       "      <td>大众敞篷家族新成员　高尔夫敞篷版实拍</td>\n",
       "      <td>在今年３月的日内瓦车展上，我们见到了高尔夫家族的新成员，高尔夫敞篷版，这款全新敞篷车受到了众...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  category                                 theme  \\\n",
       "0       汽车  新辉腾　４．２　Ｖ８　４座加长Ｉｎｄｉｖｉｄｕａｌ版２０１１款　最新报价   \n",
       "1       汽车                         ９１８　Ｓｐｙｄｅｒ概念车   \n",
       "2       汽车              日内瓦亮相　ＭＩＮＩ性能版／概念车－１．６Ｔ引擎   \n",
       "3       汽车                清仓大甩卖一汽夏利Ｎ５威志Ｖ２低至３．３９万   \n",
       "4       汽车                    大众敞篷家族新成员　高尔夫敞篷版实拍   \n",
       "\n",
       "                                             content  \n",
       "0  经销商　电话　试驾／订车Ｕ憬杭州滨江区江陵路１７８０号４００８－１１２２３３转５８６４＃保常...  \n",
       "1       呼叫热线　４００８－１００－３００　服务邮箱　ｋｆ＠ｐｅｏｐｌｅｄａｉｌｙ．ｃｏｍ．ｃｎ  \n",
       "2  ＭＩＮＩ品牌在二月曾经公布了最新的ＭＩＮＩ新概念车Ｃｌｕｂｖａｎ效果图，不过现在在日内瓦车展...  \n",
       "3  清仓大甩卖！一汽夏利Ｎ５、威志Ｖ２低至３．３９万＝日，启新中国一汽强势推出一汽夏利Ｎ５、威志...  \n",
       "4  在今年３月的日内瓦车展上，我们见到了高尔夫家族的新成员，高尔夫敞篷版，这款全新敞篷车受到了众...  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_news = pd.read_table('news_data.txt', names=['category', 'theme', 'content'], encoding='utf-8')\n",
    "df_news = df_news.dropna()\n",
    "df_news.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000, 3)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 把汽车进行分类，是属于实用类，经济类，舒适类\n",
    "\n",
    "# 查看数据量\n",
    "df_news.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用jieba分词器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "阿里巴巴集团昨日宣布，将在集团管理层面设立首席数据官岗位（Ｃｈｉｅｆ　Ｄａｔａ　Ｏｆｆｉｃｅｒ），阿里巴巴Ｂ２Ｂ公司ＣＥＯ陆兆禧将会出任上述职务，向集团ＣＥＯ马云直接汇报。＞菹ぃ和６月初的首席风险官职务任命相同，首席数据官亦为阿里巴巴集团在完成与雅虎股权谈判，推进“ｏｎｅ　ｃｏｍｐａｎｙ”目标后，在集团决策层面新增的管理岗位。０⒗锛团昨日表示，“变成一家真正意义上的数据公司”已是战略共识。记者刘夏\n"
     ]
    }
   ],
   "source": [
    "# 将数据转换成List格式\n",
    "content = df_news.content.values.tolist()\n",
    "# 随便打开一条数据，查看一下\n",
    "print(content[1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building prefix dict from the default dictionary ...\n",
      "Loading model from cache C:\\Users\\ADMINI~1\\AppData\\Local\\Temp\\jieba.cache\n",
      "Loading model cost 0.601 seconds.\n",
      "Prefix dict has been built succesfully.\n"
     ]
    }
   ],
   "source": [
    "content_S = []\n",
    "for line in content:\n",
    "    # 使用jieba对一行进行分词\n",
    "    current_segment = jieba.lcut(line)\n",
    "    # 判断是否能够分词，并且不包含换行符\n",
    "    if len(current_segment) > 1 and current_segment != '\\r\\n':  # 换行符\n",
    "        content_S.append(current_segment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['阿里巴巴',\n",
       " '集团',\n",
       " '昨日',\n",
       " '宣布',\n",
       " '，',\n",
       " '将',\n",
       " '在',\n",
       " '集团',\n",
       " '管理',\n",
       " '层面',\n",
       " '设立',\n",
       " '首席',\n",
       " '数据',\n",
       " '官',\n",
       " '岗位',\n",
       " '（',\n",
       " 'Ｃ',\n",
       " 'ｈ',\n",
       " 'ｉ',\n",
       " 'ｅ',\n",
       " 'ｆ',\n",
       " '\\u3000',\n",
       " 'Ｄ',\n",
       " 'ａ',\n",
       " 'ｔ',\n",
       " 'ａ',\n",
       " '\\u3000',\n",
       " 'Ｏ',\n",
       " 'ｆ',\n",
       " 'ｆ',\n",
       " 'ｉ',\n",
       " 'ｃ',\n",
       " 'ｅ',\n",
       " 'ｒ',\n",
       " '）',\n",
       " '，',\n",
       " '阿里巴巴',\n",
       " 'Ｂ',\n",
       " '２',\n",
       " 'Ｂ',\n",
       " '公司',\n",
       " 'Ｃ',\n",
       " 'Ｅ',\n",
       " 'Ｏ',\n",
       " '陆兆禧',\n",
       " '将',\n",
       " '会',\n",
       " '出任',\n",
       " '上述',\n",
       " '职务',\n",
       " '，',\n",
       " '向',\n",
       " '集团',\n",
       " 'Ｃ',\n",
       " 'Ｅ',\n",
       " 'Ｏ',\n",
       " '马云',\n",
       " '直接',\n",
       " '汇报',\n",
       " '。',\n",
       " '＞',\n",
       " '菹',\n",
       " 'ぃ',\n",
       " '和',\n",
       " '６',\n",
       " '月初',\n",
       " '的',\n",
       " '首席',\n",
       " '风险',\n",
       " '官',\n",
       " '职务',\n",
       " '任命',\n",
       " '相同',\n",
       " '，',\n",
       " '首席',\n",
       " '数据',\n",
       " '官亦为',\n",
       " '阿里巴巴',\n",
       " '集团',\n",
       " '在',\n",
       " '完成',\n",
       " '与',\n",
       " '雅虎',\n",
       " '股权',\n",
       " '谈判',\n",
       " '，',\n",
       " '推进',\n",
       " '“',\n",
       " 'ｏ',\n",
       " 'ｎ',\n",
       " 'ｅ',\n",
       " '\\u3000',\n",
       " 'ｃ',\n",
       " 'ｏ',\n",
       " 'ｍ',\n",
       " 'ｐ',\n",
       " 'ａ',\n",
       " 'ｎ',\n",
       " 'ｙ',\n",
       " '”',\n",
       " '目标',\n",
       " '后',\n",
       " '，',\n",
       " '在',\n",
       " '集团',\n",
       " '决策',\n",
       " '层面',\n",
       " '新增',\n",
       " '的',\n",
       " '管理',\n",
       " '岗位',\n",
       " '。',\n",
       " '０',\n",
       " '⒗',\n",
       " '锛',\n",
       " '团',\n",
       " '昨日',\n",
       " '表示',\n",
       " '，',\n",
       " '“',\n",
       " '变成',\n",
       " '一家',\n",
       " '真正',\n",
       " '意义',\n",
       " '上',\n",
       " '的',\n",
       " '数据',\n",
       " '公司',\n",
       " '”',\n",
       " '已',\n",
       " '是',\n",
       " '战略',\n",
       " '共识',\n",
       " '。',\n",
       " '记者',\n",
       " '刘夏']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content_S[1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>content_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[经销商, 　, 电话, 　, 试驾, ／, 订车, Ｕ, 憬, 杭州, 滨江区, 江陵, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[呼叫, 热线, 　, ４, ０, ０, ８, －, １, ０, ０, －, ３, ０, ０...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[Ｍ, Ｉ, Ｎ, Ｉ, 品牌, 在, 二月, 曾经, 公布, 了, 最新, 的, Ｍ, Ｉ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[清仓, 大, 甩卖, ！, 一汽, 夏利, Ｎ, ５, 、, 威志, Ｖ, ２, 低至, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[在, 今年, ３, 月, 的, 日内瓦, 车展, 上, ，, 我们, 见到, 了, 高尔夫...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           content_S\n",
       "0  [经销商, 　, 电话, 　, 试驾, ／, 订车, Ｕ, 憬, 杭州, 滨江区, 江陵, ...\n",
       "1  [呼叫, 热线, 　, ４, ０, ０, ８, －, １, ０, ０, －, ３, ０, ０...\n",
       "2  [Ｍ, Ｉ, Ｎ, Ｉ, 品牌, 在, 二月, 曾经, 公布, 了, 最新, 的, Ｍ, Ｉ...\n",
       "3  [清仓, 大, 甩卖, ！, 一汽, 夏利, Ｎ, ５, 、, 威志, Ｖ, ２, 低至, ...\n",
       "4  [在, 今年, ３, 月, 的, 日内瓦, 车展, 上, ，, 我们, 见到, 了, 高尔夫..."
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 把所有分完词，组成DataFrame格式   以Key-value的方式创建\n",
    "df_content = pd.DataFrame({'content_S':content_S})\n",
    "df_content.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stopword</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>$</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  stopword\n",
       "0        !\n",
       "1        \"\n",
       "2        #\n",
       "3        $\n",
       "4        %"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# 引入停用词表\n",
    "stopwords = pd.read_csv(\"stopwords.txt\", index_col=False, sep=\"\\t\", quoting=3, names=['stopword'], encoding='utf-8')\n",
    "stopwords.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义函数，删除语料库中的停用词\n",
    "def drop_stopwords(contents, stopwords):\n",
    "    contents_clean = []\n",
    "    all_words = []\n",
    "    for line in contents:\n",
    "        line_clean = []\n",
    "        for word in line:\n",
    "            if word in stopwords:\n",
    "                continue\n",
    "            # 过滤掉出现的停用词\n",
    "            line_clean.append(word)\n",
    "            # all_words 是为了做成词云，这里把所有的词统计出来\n",
    "            all_words.append(str(word))\n",
    "        contents_clean.append(line_clean)\n",
    "    return contents_clean, all_words\n",
    "\n",
    "contents = df_content.content_S.values.tolist()\n",
    "stopwords = stopwords.stopword.values.tolist()\n",
    "# 删除停用词\n",
    "contents_clean, all_words = drop_stopwords(contents, stopwords)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>contents_clean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[经销商, 电话, 试驾, 订车, Ｕ, 憬, 杭州, 滨江区, 江陵, 路, 号, 转, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[呼叫, 热线, 服务, 邮箱, ｋ, ｆ, ｐ, ｅ, ｏ, ｐ, ｌ, ｅ, ｄ, ａ,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[Ｍ, Ｉ, Ｎ, Ｉ, 品牌, 二月, 公布, 最新, Ｍ, Ｉ, Ｎ, Ｉ, 新, 概念...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[清仓, 甩卖, 一汽, 夏利, Ｎ, 威志, Ｖ, 低至, 万, 日, 启新, 中国, 一...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[日内瓦, 车展, 见到, 高尔夫, 家族, 新, 成员, 高尔夫, 敞篷版, 款, 全新,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      contents_clean\n",
       "0  [经销商, 电话, 试驾, 订车, Ｕ, 憬, 杭州, 滨江区, 江陵, 路, 号, 转, ...\n",
       "1  [呼叫, 热线, 服务, 邮箱, ｋ, ｆ, ｐ, ｅ, ｏ, ｐ, ｌ, ｅ, ｄ, ａ,...\n",
       "2  [Ｍ, Ｉ, Ｎ, Ｉ, 品牌, 二月, 公布, 最新, Ｍ, Ｉ, Ｎ, Ｉ, 新, 概念...\n",
       "3  [清仓, 甩卖, 一汽, 夏利, Ｎ, 威志, Ｖ, 低至, 万, 日, 启新, 中国, 一...\n",
       "4  [日内瓦, 车展, 见到, 高尔夫, 家族, 新, 成员, 高尔夫, 敞篷版, 款, 全新,..."
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将数组转换成DataFrame的格式\n",
    "df_content = pd.DataFrame({'contents_clean': contents_clean})\n",
    "df_content.head()\n",
    "# 这里的一些字母还没有清理掉，可以在停用词表中，增加一些大小写字母"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>all_words</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>经销商</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>电话</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>试驾</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>订车</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ｕ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  all_words\n",
       "0       经销商\n",
       "1        电话\n",
       "2        试驾\n",
       "3        订车\n",
       "4         Ｕ"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# all_words是去除停用词后，还有哪些词\n",
    "df_all_words = pd.DataFrame({\"all_words\": all_words})\n",
    "df_all_words.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Software\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: FutureWarning: using a dict on a Series for aggregation\n",
      "is deprecated and will be removed in a future version\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>all_words</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4150</th>\n",
       "      <td>中</td>\n",
       "      <td>5199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4282</th>\n",
       "      <td>中国</td>\n",
       "      <td>3115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88369</th>\n",
       "      <td>说</td>\n",
       "      <td>3055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51432</th>\n",
       "      <td>日</td>\n",
       "      <td>2837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104862</th>\n",
       "      <td>Ｓ</td>\n",
       "      <td>2646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       all_words  count\n",
       "4150           中   5199\n",
       "4282          中国   3115\n",
       "88369          说   3055\n",
       "51432          日   2837\n",
       "104862         Ｓ   2646"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy\n",
    "# 计算每个单词出现的次数\n",
    "words_count = df_all_words.groupby(by=['all_words'])['all_words'].agg({\"count\": numpy.size})\n",
    "# reset_index根据count排序\n",
    "words_count = words_count.reset_index().sort_values(by=[\"count\"], ascending=False)\n",
    "words_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1a1a08a7cc0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 导入词云相关包   pip install wordcloud\n",
    "from wordcloud import WordCloud\n",
    "import matplotlib.pyplot as plt \n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "matplotlib.rcParams['figure.figsize'] =  (10.0, 5.0)\n",
    "# \n",
    "wordcloud = WordCloud(font_path=\"simhei.ttf\", background_color=\"white\", max_font_size=80)\n",
    "word_frequence = {x[0]: x[1] for x in words_count.head(100).values}\n",
    "wordcloud = wordcloud.fit_words(word_frequence)\n",
    "plt.imshow(wordcloud)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TF-IDF: 提取关键字"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "法国ＶＳ西班牙、里贝里ＶＳ哈维，北京时间６月２４日凌晨一场的大战举世瞩目，而这场胜利不仅仅关乎两支顶级强队的命运，同时也是他们背后的球衣赞助商耐克和阿迪达斯之间的一次角逐。Ｔ谌胙”窘炫分薇的１６支球队之中，阿迪达斯和耐克的势力范围也是几乎旗鼓相当：其中有５家球衣由耐克提供，而阿迪达斯则赞助了６家，此外茵宝有３家，而剩下的两家则由彪马赞助。而当比赛进行到现在，率先挺进四强的两支球队分别被耐克支持的葡萄牙和阿迪达斯支持的德国占据，而由于最后一场１／４决赛是茵宝（英格兰）和彪马（意大利）的对决，这也意味着明天凌晨西班牙同法国这场阿迪达斯和耐克在１／４决赛的唯一一次直接交手将直接决定两家体育巨头在此次欧洲杯上的胜负。８据评估，在２０１２年足球商品的销售额能总共超过４０亿欧元，而单单是不足一个月的欧洲杯就有高达５亿的销售额，也就是说在欧洲杯期间将有７００万件球衣被抢购一空。根据市场评估，两大巨头阿迪达斯和耐克的市场占有率也是并驾齐驱，其中前者占据３８％，而后者占据３６％。体育权利顾问奥利弗－米歇尔在接受《队报》采访时说：“欧洲杯是耐克通过法国翻身的一个绝佳机会！”Ｃ仔尔接着谈到两大赞助商的经营策略：“竞技体育的成功会燃起球衣购买的热情，不过即便是水平相当，不同国家之间的欧洲杯效应却存在不同。在德国就很出色，大约１／４的德国人通过电视观看了比赛，而在西班牙效果则差很多，由于民族主义高涨的加泰罗尼亚地区只关注巴萨和巴萨的球衣，他们对西班牙国家队根本没什么兴趣。”因此尽管西班牙接连拿下欧洲杯和世界杯，但是阿迪达斯只为西班牙足协支付每年２６００万的赞助费＃相比之下尽管最近两届大赛表现糟糕法国足协将从耐克手中每年可以得到４０００万欧元。米歇尔解释道：“法国创纪录的４０００万欧元赞助费得益于阿迪达斯和耐克竞逐未来１５年欧洲市场的竞争。耐克需要笼络一个大国来打赢这场欧洲大陆的战争，而尽管德国拿到的赞助费并不太高，但是他们却显然牢牢掌握在民族品牌阿迪达斯手中。从长期投资来看，耐克给法国的赞助并不算过高。”\n",
      "耐克 阿迪达斯 欧洲杯 球衣 西班牙\n"
     ]
    }
   ],
   "source": [
    "import jieba.analyse\n",
    "index = 2400\n",
    "print(df_news['content'][index])\n",
    "content_S_str = \"\".join(content_S[index])\n",
    "# 关键词提取，提取出top5的关键词\n",
    "print(\" \".join(jieba.analyse.extract_tags(content_S_str, topK = 5, withWeight=False)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LDA：主题模型\n",
    "#### 格式要求：list of list形式，分词好的整个语料\n",
    "#### 每个主题的关键词所起到的重要程度是多少？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gensim库，主要适用于做自然语言处理的，里面有LDA，word3vec\n",
    "# models 是用于建模\n",
    "from gensim import corpora, models, similarities\n",
    "import gensim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 做映射，相当于词袋 首先要求输入是分好词的list\n",
    "# 拿语料库，做成字典\n",
    "dictionary = corpora.Dictionary(contents_clean)\n",
    "# 做好的词袋\n",
    "corpus = [dictionary.doc2bow(sentence) for sentence in contents_clean]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 构建模型\n",
    "lda = gensim.models.ldamodel.LdaModel(corpus=corpus, id2word=dictionary, num_topics=20) # 类似Kmeans自己指定的K值，表示最后要得到的主题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.007*\"离婚\" + 0.005*\"中\" + 0.005*\"该剧\" + 0.004*\"Ｔ\" + 0.004*\"Ｓ\"\n"
     ]
    }
   ],
   "source": [
    "# 一号分类\n",
    "print(lda.print_topic(1, topn=5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.008*\"比赛\" + 0.006*\"中\" + 0.006*\"日\" + 0.005*\"说\" + 0.004*\"志愿\"\n",
      "0.007*\"离婚\" + 0.005*\"中\" + 0.005*\"该剧\" + 0.004*\"Ｔ\" + 0.004*\"Ｓ\"\n",
      "0.018*\"饰演\" + 0.011*\"万\" + 0.011*\"号\" + 0.006*\"官兵\" + 0.005*\"戏\"\n",
      "0.006*\"万\" + 0.005*\"Ｎ\" + 0.004*\"砉\" + 0.003*\"分\" + 0.003*\"转\"\n",
      "0.009*\"孩子\" + 0.004*\"Ｍ\" + 0.004*\"Ｓ\" + 0.004*\"职场\" + 0.003*\"女人\"\n",
      "0.009*\"学校\" + 0.009*\"学生\" + 0.008*\"ａ\" + 0.008*\"ｏ\" + 0.007*\"ｎ\"\n",
      "0.015*\"男人\" + 0.009*\"女人\" + 0.007*\"说\" + 0.006*\"中\" + 0.004*\"观众\"\n",
      "0.007*\"中\" + 0.005*\"中国\" + 0.004*\"说\" + 0.004*\"比赛\" + 0.004*\"没有\"\n",
      "0.005*\"中\" + 0.005*\"中国\" + 0.004*\"日\" + 0.003*\"官兵\" + 0.003*\"时\"\n",
      "0.008*\"中\" + 0.006*\"日\" + 0.004*\"Ｉ\" + 0.004*\"中国\" + 0.004*\"Ｍ\"\n",
      "0.007*\"欧洲杯\" + 0.005*\"中\" + 0.004*\"日\" + 0.004*\"Ｓ\" + 0.004*\"巴洛特\"\n",
      "0.004*\"中\" + 0.004*\"意大利\" + 0.004*\"中国\" + 0.004*\"日\" + 0.003*\"Ｔ\"\n",
      "0.013*\"考生\" + 0.009*\"该剧\" + 0.004*\"励志\" + 0.004*\"日\" + 0.003*\"大戏\"\n",
      "0.004*\"纹身\" + 0.004*\"中\" + 0.003*\"ｍ\" + 0.003*\"台北\" + 0.003*\"皮肤\"\n",
      "0.006*\"装扮\" + 0.006*\"万\" + 0.005*\"Ｘ\" + 0.004*\"Ｓ\" + 0.004*\"号\"\n",
      "0.009*\"张绍\" + 0.004*\"同学\" + 0.003*\"说\" + 0.003*\"没有\" + 0.003*\"Ｖ\"\n",
      "0.005*\"中\" + 0.004*\"皮肤\" + 0.003*\"吃\" + 0.003*\"说\" + 0.003*\"肌肤\"\n",
      "0.010*\"中\" + 0.007*\"电影\" + 0.006*\"说\" + 0.005*\"没有\" + 0.005*\"时\"\n",
      "0.005*\"中国\" + 0.005*\"文化\" + 0.004*\"导师\" + 0.004*\"中\" + 0.004*\"发展\"\n",
      "0.042*\"ａ\" + 0.039*\"ｅ\" + 0.029*\"ｉ\" + 0.026*\"ｒ\" + 0.024*\"ｏ\"\n"
     ]
    }
   ],
   "source": [
    "for topic in lda.print_topics(num_topics=20, num_words=5):\n",
    "    print(topic[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用贝叶斯算法进行分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>contents_clean</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[经销商, 电话, 试驾, 订车, Ｕ, 憬, 杭州, 滨江区, 江陵, 路, 号, 转, ...</td>\n",
       "      <td>汽车</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[呼叫, 热线, 服务, 邮箱, ｋ, ｆ, ｐ, ｅ, ｏ, ｐ, ｌ, ｅ, ｄ, ａ,...</td>\n",
       "      <td>汽车</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[Ｍ, Ｉ, Ｎ, Ｉ, 品牌, 二月, 公布, 最新, Ｍ, Ｉ, Ｎ, Ｉ, 新, 概念...</td>\n",
       "      <td>汽车</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[清仓, 甩卖, 一汽, 夏利, Ｎ, 威志, Ｖ, 低至, 万, 日, 启新, 中国, 一...</td>\n",
       "      <td>汽车</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[日内瓦, 车展, 见到, 高尔夫, 家族, 新, 成员, 高尔夫, 敞篷版, 款, 全新,...</td>\n",
       "      <td>汽车</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      contents_clean label\n",
       "0  [经销商, 电话, 试驾, 订车, Ｕ, 憬, 杭州, 滨江区, 江陵, 路, 号, 转, ...    汽车\n",
       "1  [呼叫, 热线, 服务, 邮箱, ｋ, ｆ, ｐ, ｅ, ｏ, ｐ, ｌ, ｅ, ｄ, ａ,...    汽车\n",
       "2  [Ｍ, Ｉ, Ｎ, Ｉ, 品牌, 二月, 公布, 最新, Ｍ, Ｉ, Ｎ, Ｉ, 新, 概念...    汽车\n",
       "3  [清仓, 甩卖, 一汽, 夏利, Ｎ, 威志, Ｖ, 低至, 万, 日, 启新, 中国, 一...    汽车\n",
       "4  [日内瓦, 车展, 见到, 高尔夫, 家族, 新, 成员, 高尔夫, 敞篷版, 款, 全新,...    汽车"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = pd.DataFrame({'contents_clean': contents_clean, 'label': df_news['category']})\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['汽车', '财经', '科技', '健康', '体育', '教育', '文化', '军事', '娱乐', '时尚'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# unique是查看当前这一列有多少不重复的值\n",
    "df_train.label.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>contents_clean</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[经销商, 电话, 试驾, 订车, Ｕ, 憬, 杭州, 滨江区, 江陵, 路, 号, 转, ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[呼叫, 热线, 服务, 邮箱, ｋ, ｆ, ｐ, ｅ, ｏ, ｐ, ｌ, ｅ, ｄ, ａ,...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[Ｍ, Ｉ, Ｎ, Ｉ, 品牌, 二月, 公布, 最新, Ｍ, Ｉ, Ｎ, Ｉ, 新, 概念...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[清仓, 甩卖, 一汽, 夏利, Ｎ, 威志, Ｖ, 低至, 万, 日, 启新, 中国, 一...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[日内瓦, 车展, 见到, 高尔夫, 家族, 新, 成员, 高尔夫, 敞篷版, 款, 全新,...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      contents_clean  label\n",
       "0  [经销商, 电话, 试驾, 订车, Ｕ, 憬, 杭州, 滨江区, 江陵, 路, 号, 转, ...      1\n",
       "1  [呼叫, 热线, 服务, 邮箱, ｋ, ｆ, ｐ, ｅ, ｏ, ｐ, ｌ, ｅ, ｄ, ａ,...      1\n",
       "2  [Ｍ, Ｉ, Ｎ, Ｉ, 品牌, 二月, 公布, 最新, Ｍ, Ｉ, Ｎ, Ｉ, 新, 概念...      1\n",
       "3  [清仓, 甩卖, 一汽, 夏利, Ｎ, 威志, Ｖ, 低至, 万, 日, 启新, 中国, 一...      1\n",
       "4  [日内瓦, 车展, 见到, 高尔夫, 家族, 新, 成员, 高尔夫, 敞篷版, 款, 全新,...      1"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_mapping = {\"汽车\": 1, \"财经\": 2, \"科技\": 3, \"健康\": 4, \"体育\": 5, \"教育\": 6, \"文化\": 7, \"军事\": 8, \"娱乐\": 9, \"时尚\": 10 }\n",
    "df_train['label'] = df_train['label'].map(label_mapping)\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(df_train['contents_clean'].values, df_train['label'].values, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'上海'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看当前数据是什么\n",
    "x_train[0][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'中新网 上海 日电 记者 于俊 父亲节 网络 吃 一顿 电影 快餐 微 电影 爸 对不起 我爱你 定于 本月 日 父亲节 当天 各大 视频 网站 首映 葜 谱 鞣 剑 保慈 障蚣 钦 呓 樯 埽 ⒌ 缬 埃 ǎ 停 椋 悖 颍 铩 妫 椋 恚 称 微型 电影 新 媒体 平台 播放 状态 短时 休闲 状态 观看 完整 策划 系统 制作 体系 支持 显示 较完整 故事情节 电影 微 超短 时 放映 秒 秒 微 周期 制作 天 数周 微 规模 投资 人民币 几千 数万元 每部 内容 融合 幽默 搞怪 时尚 潮流 人文 言情 公益 教育 商业 定制 主题 单独 成篇 系列 成剧 唇 开播 微 电影 爸 对不起 我爱你 讲述 一对 父子 观念 不同 缺少 沟通 导致 关系 父亲 传统 固执 钟情 传统 生活 方式 儿子 新派 音乐 达 习惯 晚出 早 生活 性格 张扬 叛逆 两种 截然不同 生活 方式 理念 差异 一场 父子 间 拉开序幕 子 失手 打破 父亲 心爱 物品 父亲 赶出 家门 剧情 演绎 父亲节 妹妹 哥哥 化解 父亲 这场 矛盾 映逋坏 嚼 斫 狻 ⒍ 粤 ⒌ 桨容 争执 退让 传统 尴尬 父子 尴尬 情 男人 表达 心中 那份 感恩 一杯 滤挂 咖啡 父亲节 变得 温馨 镁 缬 缮 虾 Ｎ 逄 煳 幕 传播 迪欧 咖啡 联合 出品 出品人 希望 观摩 扪心自问 父亲节 父亲 记得 父亲 生日 哪一天 父亲 爱喝 跨出 家门 那一刻 感觉 一颗 颤动 心 操劳 天下 儿女 父亲节 大声 喊出 父亲 家人 爱 完'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words = []\n",
    "for line_index in range(len(x_train)):\n",
    "    try:\n",
    "        # 转换成String 的串\n",
    "        words.append(' '.join(x_train[line_index]))\n",
    "    except:\n",
    "        print(line_index, word_index)\n",
    "words[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3750\n"
     ]
    }
   ],
   "source": [
    "print(len(words))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['bird', 'cat', 'dog', 'fish']\n",
      "[[0 1 1 1]\n",
      " [0 2 1 0]\n",
      " [1 0 0 1]\n",
      " [1 0 0 0]]\n",
      "[2 3 2 2]\n"
     ]
    }
   ],
   "source": [
    "# Sklearn文本构造器\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "# 假设有四篇文章（注意，下面是是String串的形式）\n",
    "texts = [\"dog cat fish\", \"dog cat cat\", \"fish bird\", \"bird\"]\n",
    "# 文本向量构造器\n",
    "cv = CountVectorizer()\n",
    "# 按照指定的标准，进行转换\n",
    "cv_fit = cv.fit_transform(texts)\n",
    "\n",
    "# 查看不重复的单词\n",
    "print(cv.get_feature_names())\n",
    "\n",
    "# 查看文本不重复单词的词频向量\n",
    "print(cv_fit.toarray())\n",
    "\n",
    "#统计各个单词出现次数\n",
    "print(cv_fit.toarray().sum(axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['bird', 'cat', 'cat cat', 'cat fish', 'dog', 'dog cat', 'dog cat cat', 'dog cat fish', 'fish', 'fish bird']\n",
      "[[0 1 0 1 1 1 0 1 1 0]\n",
      " [0 2 1 0 1 1 1 0 0 0]\n",
      " [1 0 0 0 0 0 0 0 1 1]\n",
      " [1 0 0 0 0 0 0 0 0 0]]\n",
      "[[0 1 0 1 1 1 0 1 1 0]\n",
      " [0 2 1 0 1 1 1 0 0 0]\n",
      " [1 0 0 0 0 0 0 0 1 1]\n",
      " [1 0 0 0 0 0 0 0 0 0]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "texts = [\"dog cat fish\", \"dog cat cat\", \"fish bird\", \"bird\"]\n",
    "# 使用 ngram_range 会使用一个组合，这样会使得向量更加复杂\n",
    "cv = CountVectorizer(ngram_range=(1, 4))\n",
    "cv_fit = cv.fit_transform(texts)\n",
    "\n",
    "print(cv.get_feature_names())\n",
    "print(cv_fit.toarray())\n",
    "print(cv_fit.toarray())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CountVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
       "        dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
       "        lowercase=False, max_df=1.0, max_features=4000, min_df=1,\n",
       "        ngram_range=(1, 1), preprocessor=None, stop_words=None,\n",
       "        strip_accents=None, token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
       "        tokenizer=None, vocabulary=None)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用SKLearn中的CountVectorizer构造向量\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "# 使用 word：词 进行向量构造\n",
    "vec = CountVectorizer(analyzer='word', max_features=4000, lowercase=False)\n",
    "vec.fit(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 引入朴素贝叶斯\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "# 构造贝叶斯陌溪\n",
    "classifier = MultinomialNB()\n",
    "\n",
    "# 使用构造好的向量，进行模型训练   最后得到贝叶斯分类器\n",
    "classifier.fit(vec.transform(words), y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'国家 公务员 考试 申论 应用文 类 试题 实质 一道 集 概括 分析 提出 解决问题 一体 综合性 试题 说 一道 客观 凝练 申发 论述 文章 题目 分析 历年 国考 申论 真题 公文 类 试题 类型 多样 包括 公文 类 事务性 文书 类 题材 从题 干 作答 材料 内容 整合 分析 无需 太 创造性 发挥 纵观 历年 申论 真题 作答 应用文 类 试题 没有 文种 格式 作出 特别 重在 内容 考查 不是 行文 格式 考生 平常心 面对 应用文 类 试题 准确 把握 作答 领会 内在 含义 把握 题材 主旨 材料 结构 轻松 应对 应用文 类 试题 Ｒ 弧 ⒆ 钒 盐 展文 写作 原则 Ｔ 材料 中来 应用文 类 试题 材料 总体 把握 客观 考生 材料 中来 材料 中 把握 材料 准确 理解 题材 主旨 Ｔ 政府 角度 作答 应用文 类 试题 更应 注重 政府 角度 观点 政府 角度 出发 原则 表述 观点 提出 解决 之策 考生 作答 时 站 政府 人员 角度 看待 提出 解决问题 Ｔ 文体 结构 形式 不是 考查 重点 文体 结构 大部分 评分 时 关键点 解答 方法 薄 ⒆ ス 丶 词 明 方向 作答 题目 题干 作答 作答 方向 作答 角度 关键 向导 考生 仔细阅读 题干 作答 抓住 关键词 作答 方向 相关 要点 整理 作答 思路 年国考 地市级 真 题为 例 潦惺姓 府 宣传 推进 近海 水域 污染 整治 工作 请 给定 资料 市政府 工作人员 身份 草拟 一份 宣传 纲要 分 Ｒ 求 保对 宣传 内容 要点 提纲挈领 陈述 玻 体现 政府 精神 全市 各界 关心 支持 污染 整治 工作 通俗易懂 超过 字 肮 丶 词 近海 水域 污染 整治 工作 市政府 工作人员 身份 宣传 纲要 提纲挈领 陈述 体现 政府 精神 全市 各界 关心 支持 污染 整治 工作 通俗易懂 提示 归结 作答 要点 包括 污染 情况 原因 解决 对策 作答 思路 情况 原因 对策 意义 逻辑 顺序 安排 文章 结构 病 ⒋ 缶殖 龇 ⅲ 明 结构 解答 应用文 类 试题 考生 材料 整体 出发 大局 出发 高屋建瓴 把握 材料 主题 思想 事件 起因 解决 对策 阅读文章 构建 文章 结构 直至 快速 解答 场 ⒗ 硭 乘悸 罚明 逻辑 应用文 类 试题 严密 逻辑思维 情况 原因 对策 意义 考生 作答 先 弄清楚 解答 思路 统筹安排 脉络 清晰 逻辑 表达 内容 表述 础 把握 明 详略 考生 仔细阅读 分析 揣摩 应用文 类 试题 内容 答题 时要 详略 得当 主次 分明 安排 内容 增加 文章 层次感 阅卷 老师 阅卷 时能 明白 清晰 一目了然 玻埃 保蹦旯 考 考试 申论 试卷 分为 省级 地市级 两套 试卷 能力 大有 不同 省级 申论 试题 考生 宏观 角度看 注重 深度 广度 考生 深谋远虑 地市级 试题 考生 微观 视角 观察 侧重 考查 解决 能力 考生 贯彻执行 作答 时 区别对待'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_words = []\n",
    "for line_index in range(len(x_test)):\n",
    "    try:\n",
    "        test_words.append( ' '.join(x_test[line_index]))\n",
    "    except:\n",
    "        print(line_index, word_index)\n",
    "test_words[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.804"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.score(vec.transform(test_words), y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TfidfVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
       "        dtype=<class 'numpy.float64'>, encoding='utf-8', input='content',\n",
       "        lowercase=False, max_df=1.0, max_features=4000, min_df=1,\n",
       "        ngram_range=(1, 1), norm='l2', preprocessor=None, smooth_idf=True,\n",
       "        stop_words=None, strip_accents=None, sublinear_tf=False,\n",
       "        token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b', tokenizer=None, use_idf=True,\n",
       "        vocabulary=None)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "vectorizer = TfidfVectorizer(analyzer='word', max_features=4000, lowercase= False)\n",
    "vectorizer.fit(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "classifier = MultinomialNB()\n",
    "classifier.fit(vectorizer.transform(words), y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8136"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.score(vectorizer.transform(test_words), y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
